Identification and Decompositions in Probit and Logit Models
Chung Choe (),
SeEun Jung () and
No 10530, IZA Discussion Papers from Institute for the Study of Labor (IZA)
Probit and logit models typically require a normalization on the error variance for model identification. This paper shows that in the context of sample mean probability decompositions, error variance normalizations preclude estimation of the effects of group differences in the latent variable model parameters. An empirical example is provided for a model in which the error variances are identified. This identification allows the effects of group differences in the latent variable model parameters to be estimated.
Keywords: decompositions; probit; logit; identification (search for similar items in EconPapers)
JEL-codes: C35 J16 D81 J71 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dcm, nep-ecm and nep-ore
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Working Paper: Identification and Decompositions in Probit and Logit Models (2017)
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